wehub-resource-sync
57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
import mlflow
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_SAMPLE_TRACE = {
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"info": {
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"request_id": "2e72d64369624e6888324462b62dc120",
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"experiment_id": "0",
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"timestamp_ms": 1726145090860,
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"execution_time_ms": 162,
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"status": "OK",
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"request_metadata": {
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"mlflow.trace_schema.version": "2",
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"mlflow.traceInputs": '{"x": 1}',
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"mlflow.traceOutputs": '{"prediction": 1}',
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},
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"tags": {
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"fruit": "apple",
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"food": "pizza",
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},
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},
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"data": {
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"spans": [
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{
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"name": "remote",
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"context": {
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"span_id": "0x337af925d6629c01",
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"trace_id": "0x05e82d1fc4486f3986fae6dd7b5352b1",
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},
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"parent_id": None,
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"start_time": 1726145091022155863,
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"end_time": 1726145091022572053,
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"status_code": "OK",
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"status_message": "",
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"attributes": {
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"mlflow.traceRequestId": '"2e72d64369624e6888324462b62dc120"',
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"mlflow.spanType": '"UNKNOWN"',
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"mlflow.spanInputs": '{"x": 1}',
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"mlflow.spanOutputs": '{"prediction": 1}',
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},
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"events": [
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{"name": "event", "timestamp": 1726145091022287, "attributes": {"foo": "bar"}}
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],
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},
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],
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"request": '{"x": 1}',
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"response": '{"prediction": 1}',
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},
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}
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class Model(mlflow.pyfunc.PythonModel):
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def predict(self, context, model_input):
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mlflow.add_trace(_SAMPLE_TRACE)
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return 1
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mlflow.models.set_model(Model())
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